This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, these statements are to be read in this light and are not to be understood as admissions about what is or is not prior art.
In recent years, the prices of image sensors (i.e., cameras) have dropped significantly making cameras as one the most ubiquitous sensing devices deployed throughout the world. Camera can capture a wide range of information. Cameras can be used to monitor traffic flow, view wildlife, detect intruders, detect anomalies, or determine weather conditions. Millions of network cameras are now connected to the Internet for a variety of purposes. Currently, nearly two hundred million network cameras have been deployed. Real-time visual data can be used in many applications, such as emergency responses, in a real-time manner. Some of these data streams are publicly available without password protection. As researchers gain the ability to collect large amounts of visual data about the world, the true potential of data-driven research is recognized. With the emergence of new machine-learning technologies, a wealth of previously untapped potential for large-scale visual data analysis has surfaced. In 2020, 75% of mobile device data traffic will be video and 82% of internet protocol (IP), i.e., non-mobile, traffic will be video. Visual data is substantial because one high definition (HD) video camera can produce data orders of magnitude faster than text data. Computer vision is becoming one of the central data-analysis techniques driving big-data research. Visual data (image and video) provides a high level of utility because of the versatility and the rich information it provides. A single image may reveal many different types of information. For example, routine traffic camera can provide information about suspects in a bank robbery, or a terrorist attack.
Despite the large amount of real-time data publicly available via publicly accessible cameras, two major challenges inhibit the true potential of analyzing the real-time data from these cameras. The first challenge is identifying these cameras either statically or dynamically on the fly (i.e., real-time). This challenge is particularly problematic since there is a wide range of protocols used to retrieve data from network cameras, since different brands of network cameras need different retrieval methods (for example, different paths for hypertext transfer protocol (HTTP) GET commands). While to date there are samplings of various databases including small number of cameras, there is not a global database, in particular one which can be dynamically populated on the fly.
A second challenge is a need for contextual information. Context could include the camera's location, refresh rate, whether it is indoor or outdoor, and so on. This contextual information is called metadata and can be helpful for data analytics. Metadata can be useful for identifying the cameras for specific purposes, for example, traffic cameras for studying urban transportation. However, to date prior art methods and system have not provided the capability to identify at a global scale camera metadata, once each camera has been identified.
Therefore, there is an unmet need for a novel system and method that can i) identify publicly available cameras at a global level either statically for populating a database, or dynamically on the fly; and ii) determine metadata associated with those cameras either statically for populating a database, or dynamically on the fly.